23 citations · 58 across the 12 of their papers we have counts for
12 papers
Continual Learning From a Stream of APIs
Enneng Yang, Zhenyi Wang, Li Shen +5
Continual learning (CL) aims to learn new tasks without forgetting previous tasks. However, existing CL methods require a large amount of raw data, which is often unavailable due t…
Distributionally Robust Cross Subject EEG Decoding
Tiehang Duan, Zhenyi Wang, Gianfranco Doretto +3
Recently, deep learning has shown to be effective for Electroencephalography (EEG) decoding tasks. Yet, its performance can be negatively influenced by two key factors: 1) the high…
A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning
Zhenyi Wang, Enneng Yang, Li Shen +1
Forgetting refers to the loss or deterioration of previously acquired knowledge. While existing surveys on forgetting have primarily focused on continual learning, forgetting is a…
Learning to Learn from APIs: Black-Box Data-Free Meta-Learning
Zixuan Hu, Li Shen, Zhenyi Wang +3
Data-free meta-learning (DFML) aims to enable efficient learning of new tasks by meta-learning from a collection of pre-trained models without access to the training data. Existing…
Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning
Zixuan Hu, Li Shen, Zhenyi Wang +3
The goal of data-free meta-learning is to learn useful prior knowledge from a collection of pre-trained models without accessing their training data. However, existing works only s…
Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions
Zhenyi Wang, Li Shen, Le Fang +4
The paradigm of machine intelligence moves from purely supervised learning to a more practical scenario when many loosely related unlabeled data are available and labeled data is s…
Improving Task-free Continual Learning by Distributionally Robust Memory Evolution
Zhenyi Wang, Li Shen, Le Fang +3
Task-free continual learning (CL) aims to learn a non-stationary data stream without explicit task definitions and not forget previous knowledge. The widely adopted memory replay a…
Uncertainty Detection and Reduction in Neural Decoding of EEG Signals
Tiehang Duan, Zhenyi Wang, Sheng Liu +2
EEG decoding systems based on deep neural networks have been widely used in decision making of brain computer interfaces (BCI). Their predictions, however, can be unreliable given…
Repulsive Attention: Rethinking Multi-head Attention as Bayesian Inference
Bang An, Jie Lyu, Zhenyi Wang +6
The neural attention mechanism plays an important role in many natural language processing applications. In particular, the use of multi-head attention extends single-head attentio…
Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints
Zhenyi Wang, Xiaoyang Wang, Bang An +2
Text generation from a knowledge base aims to translate knowledge triples to natural language descriptions. Most existing methods ignore the faithfulness between a generated text d…
Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions
Zhenyi Wang, Ping Yu, Yang Zhao +4
Human-motion generation is a long-standing challenging task due to the requirement of accurately modeling complex and diverse dynamic patterns. Most existing methods adopt sequence…
Location Augmentation for CNN
Zhenyi Wang, Olga Veksler
CNNs have made a tremendous impact on the field of computer vision in the last several years. The main component of any CNN architecture is the convolution operation, which is tran…